docs: def _getMultiClassMap(self):
          """ Relief algorithms handle the scoring updates a little differently for data with multiclass outcomes. In ReBATE we implement multiclass scoring in line with
          the strategy described by Kononenko 1994 within the RELIEF-F variant which was suggested to outperform the RELIEF-E multiclass variant. This strategy weights
          score updates derived from misses of different classes by the class frequency observed in the training data. 'The idea is that the algorithm should estimate the
          ability of attributes to separate each pair of classes regardless of which two classes are closest to each other'.  In this method we prepare for this normalization
          by creating a class dictionary, and storing respective class frequencies. This is needed for ReliefF multiclass score update normalizations. """
          mcmap = dict()
  
          for i in range(self._datalen):
              if(self._y[i] not in mcmap):
                  mcmap[self._y[i]] = 0
              else:
                  mcmap[self._y[i]] += 1
  
          for each in self._label_list:
              mcmap[each] = mcmap[each]/float(self._datalen)
  
          return mcmap